code: def test_predict_non_gaussian(
      n_samples, n_features, n_neighbors, n_query
  ):
      np.random.seed(123)
  
      X_host_train = pd.DataFrame(
          np.random.uniform(0, 1, (n_samples, n_features))
      )
      y_host_train = pd.DataFrame(
          np.random.randint(0, 5, (n_samples, 1))
      )
      X_host_test = pd.DataFrame(
          np.random.uniform(0, 1, (n_query, n_features))
      )
  
      X_device_train = cudf.DataFrame.from_pandas(X_host_train)
      y_device_train = cudf.DataFrame.from_pandas(y_host_train)
  
      X_device_test = cudf.DataFrame.from_pandas(X_host_test)
  
      knn_sk = skKNN(
          algorithm="brute", n_neighbors=n_neighbors, n_jobs=1
      )
      knn_sk.fit(X_host_train, y_host_train.values.ravel())
  
      sk_result = knn_sk.predict(X_host_test)
  
      knn_cuml = cuKNN(n_neighbors=n_neighbors)
      knn_cuml.fit(X_device_train, y_device_train)
  
      with cuml.using_output_type("numpy"):
          cuml_result = knn_cuml.predict(X_device_test)
  
          assert np.array_equal(cuml_result, sk_result)
  
file_path: cuml/python/cuml/tests/test_kneighbors_classifier.py
imports: from cuml.testing.utils import array_equal
  from cuml.internals.safe_imports import cpu_only_import
  from sklearn.datasets import make_blobs
  from sklearn.neighbors import KNeighborsClassifier
  from cuml.neighbors import KNeighborsClassifier
  import cuml
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 7 for calling a global function with example usage: test_predict_non_gaussian(n_samples, n_features, n_neighbors, n_query) without return types